Tag Archives: environment

Parrot 4.0 is out

Post Syndicated from ris original https://lwn.net/Articles/755095/rss

Parrot 4.0 has been released. Parrot
is a security-oriented distribution aimed at penetration tests and digital
forensics analysis, with additional tools to preserve privacy. “On
Parrot 4.0 we decided to provide netinstall images too as we would like
people to use Parrot not only as a pentest distribution, but also as a
framework to build their very own working environment with ease.

Docker templates are also available.

UK soldiers design Raspberry Pi bomb disposal robot

Post Syndicated from Helen Lynn original https://www.raspberrypi.org/blog/uk-soldiers-design-raspberry-pi-bomb-disposal-robot/

Three soldiers in the British Army have used a Raspberry Pi to build an autonomous robot, as part of their Foreman of Signals course.

Meet The Soldiers Revolutionising Bomb Disposal

Three soldiers from Blandford Camp have successfully designed and built an autonomous robot as part of their Foreman of Signals Course at the Dorset Garrison.

Autonomous robots

Forces Radio BFBS carried a story last week about Staff Sergeant Jolley, Sergeant Rana, and Sergeant Paddon, also known as the “Project ROVER” team. As part of their Foreman of Signals training, their task was to design an autonomous robot that can move between two specified points, take a temperature reading, and transmit the information to a remote computer. The team comments that, while semi-autonomous robots have been used as far back as 9/11 for tasks like finding people trapped under rubble, nothing like their robot and on a similar scale currently exists within the British Army.

The ROVER buggy

Their build is named ROVER, which stands for Remote Obstacle aVoiding Environment Robot. It’s a buggy that moves on caterpillar tracks, and it’s tethered; we wonder whether that might be because it doesn’t currently have an on-board power supply. A demo shows the robot moving forward, then changing its path when it encounters an obstacle. The team is using RealVNC‘s remote access software to allow ROVER to send data back to another computer.

Applications for ROVER

Dave Ball, Senior Lecturer in charge of the Foreman of Signals course, comments that the project is “a fantastic opportunity for [the team] to, even only halfway through the course, showcase some of the stuff they’ve learnt and produce something that’s really quite exciting.” The Project ROVER team explains that the possibilities for autonomous robots like this one are extensive: they include mine clearance, bomb disposal, and search-and-rescue campaigns. They point out that existing semi-autonomous hardware is not as easy to program as their build. In contrast, they say, “with the invention of the Raspberry Pi, this has allowed three very inexperienced individuals to program a robot very capable of doing these things.”

We make Raspberry Pi computers because we want building things with technology to be as accessible as possible. So it’s great to see a project like this, made by people who aren’t techy and don’t have a lot of computing experience, but who want to solve a problem and see that the Pi is an affordable and powerful tool that can help.

The post UK soldiers design Raspberry Pi bomb disposal robot appeared first on Raspberry Pi.

Amazon Sumerian – Now Generally Available

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/amazon-sumerian-now-generally-available/

We announced Amazon Sumerian at AWS re:Invent 2017. As you can see from Tara‘s blog post (Presenting Amazon Sumerian: An Easy Way to Create VR, AR, and 3D Experiences), Sumerian does not require any specialized programming or 3D graphics expertise. You can build VR, AR, and 3D experiences for a wide variety of popular hardware platforms including mobile devices, head-mounted displays, digital signs, and web browsers.

I’m happy to announce that Sumerian is now generally available. You can create realistic virtual environments and scenes without having to acquire or master specialized tools for 3D modeling, animation, lighting, audio editing, or programming. Once built, you can deploy your finished creation across multiple platforms without having to write custom code or deal with specialized deployment systems and processes.

Sumerian gives you a web-based editor that you can use to quickly and easily create realistic, professional-quality scenes. There’s a visual scripting tool that lets you build logic to control how objects and characters (Sumerian Hosts) respond to user actions. Sumerian also lets you create rich, natural interactions powered by AWS services such as Amazon Lex, Polly, AWS Lambda, AWS IoT, and Amazon DynamoDB.

Sumerian was designed to work on multiple platforms. The VR and AR apps that you create in Sumerian will run in browsers that supports WebGL or WebVR and on popular devices such as the Oculus Rift, HTC Vive, and those powered by iOS or Android.

During the preview period, we have been working with a broad spectrum of customers to put Sumerian to the test and to create proof of concept (PoC) projects designed to highlight an equally broad spectrum of use cases, including employee education, training simulations, field service productivity, virtual concierge, design and creative, and brand engagement. Fidelity Labs (the internal R&D unit of Fidelity Investments), was the first to use a Sumerian host to create an engaging VR experience. Cora (the host) lives within a virtual chart room. She can display stock quotes, pull up company charts, and answer questions about a company’s performance. This PoC uses Amazon Polly to implement text to speech and Amazon Lex for conversational chatbot functionality. Read their blog post and watch the video inside to see Cora in action:

Now that Sumerian is generally available, you have the power to create engaging AR, VR, and 3D experiences of your own. To learn more, visit the Amazon Sumerian home page and then spend some quality time with our extensive collection of Sumerian Tutorials.

Jeff;

 

Analyze Apache Parquet optimized data using Amazon Kinesis Data Firehose, Amazon Athena, and Amazon Redshift

Post Syndicated from Roy Hasson original https://aws.amazon.com/blogs/big-data/analyzing-apache-parquet-optimized-data-using-amazon-kinesis-data-firehose-amazon-athena-and-amazon-redshift/

Amazon Kinesis Data Firehose is the easiest way to capture and stream data into a data lake built on Amazon S3. This data can be anything—from AWS service logs like AWS CloudTrail log files, Amazon VPC Flow Logs, Application Load Balancer logs, and others. It can also be IoT events, game events, and much more. To efficiently query this data, a time-consuming ETL (extract, transform, and load) process is required to massage and convert the data to an optimal file format, which increases the time to insight. This situation is less than ideal, especially for real-time data that loses its value over time.

To solve this common challenge, Kinesis Data Firehose can now save data to Amazon S3 in Apache Parquet or Apache ORC format. These are optimized columnar formats that are highly recommended for best performance and cost-savings when querying data in S3. This feature directly benefits you if you use Amazon Athena, Amazon Redshift, AWS Glue, Amazon EMR, or any other big data tools that are available from the AWS Partner Network and through the open-source community.

Amazon Connect is a simple-to-use, cloud-based contact center service that makes it easy for any business to provide a great customer experience at a lower cost than common alternatives. Its open platform design enables easy integration with other systems. One of those systems is Amazon Kinesis—in particular, Kinesis Data Streams and Kinesis Data Firehose.

What’s really exciting is that you can now save events from Amazon Connect to S3 in Apache Parquet format. You can then perform analytics using Amazon Athena and Amazon Redshift Spectrum in real time, taking advantage of this key performance and cost optimization. Of course, Amazon Connect is only one example. This new capability opens the door for a great deal of opportunity, especially as organizations continue to build their data lakes.

Amazon Connect includes an array of analytics views in the Administrator dashboard. But you might want to run other types of analysis. In this post, I describe how to set up a data stream from Amazon Connect through Kinesis Data Streams and Kinesis Data Firehose and out to S3, and then perform analytics using Athena and Amazon Redshift Spectrum. I focus primarily on the Kinesis Data Firehose support for Parquet and its integration with the AWS Glue Data Catalog, Amazon Athena, and Amazon Redshift.

Solution overview

Here is how the solution is laid out:

 

 

The following sections walk you through each of these steps to set up the pipeline.

1. Define the schema

When Kinesis Data Firehose processes incoming events and converts the data to Parquet, it needs to know which schema to apply. The reason is that many times, incoming events contain all or some of the expected fields based on which values the producers are advertising. A typical process is to normalize the schema during a batch ETL job so that you end up with a consistent schema that can easily be understood and queried. Doing this introduces latency due to the nature of the batch process. To overcome this issue, Kinesis Data Firehose requires the schema to be defined in advance.

To see the available columns and structures, see Amazon Connect Agent Event Streams. For the purpose of simplicity, I opted to make all the columns of type String rather than create the nested structures. But you can definitely do that if you want.

The simplest way to define the schema is to create a table in the Amazon Athena console. Open the Athena console, and paste the following create table statement, substituting your own S3 bucket and prefix for where your event data will be stored. A Data Catalog database is a logical container that holds the different tables that you can create. The default database name shown here should already exist. If it doesn’t, you can create it or use another database that you’ve already created.

CREATE EXTERNAL TABLE default.kfhconnectblog (
  awsaccountid string,
  agentarn string,
  currentagentsnapshot string,
  eventid string,
  eventtimestamp string,
  eventtype string,
  instancearn string,
  previousagentsnapshot string,
  version string
)
STORED AS parquet
LOCATION 's3://your_bucket/kfhconnectblog/'
TBLPROPERTIES ("parquet.compression"="SNAPPY")

That’s all you have to do to prepare the schema for Kinesis Data Firehose.

2. Define the data streams

Next, you need to define the Kinesis data streams that will be used to stream the Amazon Connect events.  Open the Kinesis Data Streams console and create two streams.  You can configure them with only one shard each because you don’t have a lot of data right now.

3. Define the Kinesis Data Firehose delivery stream for Parquet

Let’s configure the Data Firehose delivery stream using the data stream as the source and Amazon S3 as the output. Start by opening the Kinesis Data Firehose console and creating a new data delivery stream. Give it a name, and associate it with the Kinesis data stream that you created in Step 2.

As shown in the following screenshot, enable Record format conversion (1) and choose Apache Parquet (2). As you can see, Apache ORC is also supported. Scroll down and provide the AWS Glue Data Catalog database name (3) and table names (4) that you created in Step 1. Choose Next.

To make things easier, the output S3 bucket and prefix fields are automatically populated using the values that you defined in the LOCATION parameter of the create table statement from Step 1. Pretty cool. Additionally, you have the option to save the raw events into another location as defined in the Source record S3 backup section. Don’t forget to add a trailing forward slash “ / “ so that Data Firehose creates the date partitions inside that prefix.

On the next page, in the S3 buffer conditions section, there is a note about configuring a large buffer size. The Parquet file format is highly efficient in how it stores and compresses data. Increasing the buffer size allows you to pack more rows into each output file, which is preferred and gives you the most benefit from Parquet.

Compression using Snappy is automatically enabled for both Parquet and ORC. You can modify the compression algorithm by using the Kinesis Data Firehose API and update the OutputFormatConfiguration.

Be sure to also enable Amazon CloudWatch Logs so that you can debug any issues that you might run into.

Lastly, finalize the creation of the Firehose delivery stream, and continue on to the next section.

4. Set up the Amazon Connect contact center

After setting up the Kinesis pipeline, you now need to set up a simple contact center in Amazon Connect. The Getting Started page provides clear instructions on how to set up your environment, acquire a phone number, and create an agent to accept calls.

After setting up the contact center, in the Amazon Connect console, choose your Instance Alias, and then choose Data Streaming. Under Agent Event, choose the Kinesis data stream that you created in Step 2, and then choose Save.

At this point, your pipeline is complete.  Agent events from Amazon Connect are generated as agents go about their day. Events are sent via Kinesis Data Streams to Kinesis Data Firehose, which converts the event data from JSON to Parquet and stores it in S3. Athena and Amazon Redshift Spectrum can simply query the data without any additional work.

So let’s generate some data. Go back into the Administrator console for your Amazon Connect contact center, and create an agent to handle incoming calls. In this example, I creatively named mine Agent One. After it is created, Agent One can get to work and log into their console and set their availability to Available so that they are ready to receive calls.

To make the data a bit more interesting, I also created a second agent, Agent Two. I then made some incoming and outgoing calls and caused some failures to occur, so I now have enough data available to analyze.

5. Analyze the data with Athena

Let’s open the Athena console and run some queries. One thing you’ll notice is that when we created the schema for the dataset, we defined some of the fields as Strings even though in the documentation they were complex structures.  The reason for doing that was simply to show some of the flexibility of Athena to be able to parse JSON data. However, you can define nested structures in your table schema so that Kinesis Data Firehose applies the appropriate schema to the Parquet file.

Let’s run the first query to see which agents have logged into the system.

The query might look complex, but it’s fairly straightforward:

WITH dataset AS (
  SELECT 
    from_iso8601_timestamp(eventtimestamp) AS event_ts,
    eventtype,
    -- CURRENT STATE
    json_extract_scalar(
      currentagentsnapshot,
      '$.agentstatus.name') AS current_status,
    from_iso8601_timestamp(
      json_extract_scalar(
        currentagentsnapshot,
        '$.agentstatus.starttimestamp')) AS current_starttimestamp,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.firstname') AS current_firstname,
    json_extract_scalar(
      currentagentsnapshot,
      '$.configuration.lastname') AS current_lastname,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.username') AS current_username,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.routingprofile.defaultoutboundqueue.name') AS               current_outboundqueue,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.routingprofile.inboundqueues[0].name') as current_inboundqueue,
    -- PREVIOUS STATE
    json_extract_scalar(
      previousagentsnapshot, 
      '$.agentstatus.name') as prev_status,
    from_iso8601_timestamp(
      json_extract_scalar(
        previousagentsnapshot, 
       '$.agentstatus.starttimestamp')) as prev_starttimestamp,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.firstname') as prev_firstname,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.lastname') as prev_lastname,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.username') as prev_username,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.routingprofile.defaultoutboundqueue.name') as current_outboundqueue,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.routingprofile.inboundqueues[0].name') as prev_inboundqueue
  from kfhconnectblog
  where eventtype <> 'HEART_BEAT'
)
SELECT
  current_status as status,
  current_username as username,
  event_ts
FROM dataset
WHERE eventtype = 'LOGIN' AND current_username <> ''
ORDER BY event_ts DESC

The query output looks something like this:

Here is another query that shows the sessions each of the agents engaged with. It tells us where they were incoming or outgoing, if they were completed, and where there were missed or failed calls.

WITH src AS (
  SELECT
     eventid,
     json_extract_scalar(currentagentsnapshot, '$.configuration.username') as username,
     cast(json_extract(currentagentsnapshot, '$.contacts') AS ARRAY(JSON)) as c,
     cast(json_extract(previousagentsnapshot, '$.contacts') AS ARRAY(JSON)) as p
  from kfhconnectblog
),
src2 AS (
  SELECT *
  FROM src CROSS JOIN UNNEST (c, p) AS contacts(c_item, p_item)
),
dataset AS (
SELECT 
  eventid,
  username,
  json_extract_scalar(c_item, '$.contactid') as c_contactid,
  json_extract_scalar(c_item, '$.channel') as c_channel,
  json_extract_scalar(c_item, '$.initiationmethod') as c_direction,
  json_extract_scalar(c_item, '$.queue.name') as c_queue,
  json_extract_scalar(c_item, '$.state') as c_state,
  from_iso8601_timestamp(json_extract_scalar(c_item, '$.statestarttimestamp')) as c_ts,
  
  json_extract_scalar(p_item, '$.contactid') as p_contactid,
  json_extract_scalar(p_item, '$.channel') as p_channel,
  json_extract_scalar(p_item, '$.initiationmethod') as p_direction,
  json_extract_scalar(p_item, '$.queue.name') as p_queue,
  json_extract_scalar(p_item, '$.state') as p_state,
  from_iso8601_timestamp(json_extract_scalar(p_item, '$.statestarttimestamp')) as p_ts
FROM src2
)
SELECT 
  username,
  c_channel as channel,
  c_direction as direction,
  p_state as prev_state,
  c_state as current_state,
  c_ts as current_ts,
  c_contactid as id
FROM dataset
WHERE c_contactid = p_contactid
ORDER BY id DESC, current_ts ASC

The query output looks similar to the following:

6. Analyze the data with Amazon Redshift Spectrum

With Amazon Redshift Spectrum, you can query data directly in S3 using your existing Amazon Redshift data warehouse cluster. Because the data is already in Parquet format, Redshift Spectrum gets the same great benefits that Athena does.

Here is a simple query to show querying the same data from Amazon Redshift. Note that to do this, you need to first create an external schema in Amazon Redshift that points to the AWS Glue Data Catalog.

SELECT 
  eventtype,
  json_extract_path_text(currentagentsnapshot,'agentstatus','name') AS current_status,
  json_extract_path_text(currentagentsnapshot, 'configuration','firstname') AS current_firstname,
  json_extract_path_text(currentagentsnapshot, 'configuration','lastname') AS current_lastname,
  json_extract_path_text(
    currentagentsnapshot,
    'configuration','routingprofile','defaultoutboundqueue','name') AS current_outboundqueue,
FROM default_schema.kfhconnectblog

The following shows the query output:

Summary

In this post, I showed you how to use Kinesis Data Firehose to ingest and convert data to columnar file format, enabling real-time analysis using Athena and Amazon Redshift. This great feature enables a level of optimization in both cost and performance that you need when storing and analyzing large amounts of data. This feature is equally important if you are investing in building data lakes on AWS.

 


Additional Reading

If you found this post useful, be sure to check out Analyzing VPC Flow Logs with Amazon Kinesis Firehose, Amazon Athena, and Amazon QuickSight and Work with partitioned data in AWS Glue.


About the Author

Roy Hasson is a Global Business Development Manager for AWS Analytics. He works with customers around the globe to design solutions to meet their data processing, analytics and business intelligence needs. Roy is big Manchester United fan cheering his team on and hanging out with his family.

 

 

 

Securing Your Cryptocurrency

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/backing-up-your-cryptocurrency/

Securing Your Cryptocurrency

In our blog post on Tuesday, Cryptocurrency Security Challenges, we wrote about the two primary challenges faced by anyone interested in safely and profitably participating in the cryptocurrency economy: 1) make sure you’re dealing with reputable and ethical companies and services, and, 2) keep your cryptocurrency holdings safe and secure.

In this post, we’re going to focus on how to make sure you don’t lose any of your cryptocurrency holdings through accident, theft, or carelessness. You do that by backing up the keys needed to sell or trade your currencies.

$34 Billion in Lost Value

Of the 16.4 million bitcoins said to be in circulation in the middle of 2017, close to 3.8 million may have been lost because their owners no longer are able to claim their holdings. Based on today’s valuation, that could total as much as $34 billion dollars in lost value. And that’s just bitcoins. There are now over 1,500 different cryptocurrencies, and we don’t know how many of those have been misplaced or lost.



Now that some cryptocurrencies have reached (at least for now) staggering heights in value, it’s likely that owners will be more careful in keeping track of the keys needed to use their cryptocurrencies. For the ones already lost, however, the owners have been separated from their currencies just as surely as if they had thrown Benjamin Franklins and Grover Clevelands over the railing of a ship.

The Basics of Securing Your Cryptocurrencies

In our previous post, we reviewed how cryptocurrency keys work, and the common ways owners can keep track of them. A cryptocurrency owner needs two keys to use their currencies: a public key that can be shared with others is used to receive currency, and a private key that must be kept secure is used to spend or trade currency.

Many wallets and applications allow the user to require extra security to access them, such as a password, or iris, face, or thumb print scan. If one of these options is available in your wallets, take advantage of it. Beyond that, it’s essential to back up your wallet, either using the backup feature built into some applications and wallets, or manually backing up the data used by the wallet. When backing up, it’s a good idea to back up the entire wallet, as some wallets require additional private data to operate that might not be apparent.

No matter which backup method you use, it is important to back up often and have multiple backups, preferable in different locations. As with any valuable data, a 3-2-1 backup strategy is good to follow, which ensures that you’ll have a good backup copy if anything goes wrong with one or more copies of your data.

One more caveat, don’t reuse passwords. This applies to all of your accounts, but is especially important for something as critical as your finances. Don’t ever use the same password for more than one account. If security is breached on one of your accounts, someone could connect your name or ID with other accounts, and will attempt to use the password there, as well. Consider using a password manager such as LastPass or 1Password, which make creating and using complex and unique passwords easy no matter where you’re trying to sign in.

Approaches to Backing Up Your Cryptocurrency Keys

There are numerous ways to be sure your keys are backed up. Let’s take them one by one.

1. Automatic backups using a backup program

If you’re using a wallet program on your computer, for example, Bitcoin Core, it will store your keys, along with other information, in a file. For Bitcoin Core, that file is wallet.dat. Other currencies will use the same or a different file name and some give you the option to select a name for the wallet file.

To back up the wallet.dat or other wallet file, you might need to tell your backup program to explicitly back up that file. Users of Backblaze Backup don’t have to worry about configuring this, since by default, Backblaze Backup will back up all data files. You should determine where your particular cryptocurrency, wallet, or application stores your keys, and make sure the necessary file(s) are backed up if your backup program requires you to select which files are included in the backup.

Backblaze B2 is an option for those interested in low-cost and high security cloud storage of their cryptocurrency keys. Backblaze B2 supports 2-factor verification for account access, works with a number of apps that support automatic backups with encryption, error-recovery, and versioning, and offers an API and command-line interface (CLI), as well. The first 10GB of storage is free, which could be all one needs to store encrypted cryptocurrency keys.

2. Backing up by exporting keys to a file

Apps and wallets will let you export your keys from your app or wallet to a file. Once exported, your keys can be stored on a local drive, USB thumb drive, DAS, NAS, or in the cloud with any cloud storage or sync service you wish. Encrypting the file is strongly encouraged — more on that later. If you use 1Password or LastPass, or other secure notes program, you also could store your keys there.

3. Backing up by saving a mnemonic recovery seed

A mnemonic phrase, mnemonic recovery phrase, or mnemonic seed is a list of words that stores all the information needed to recover a cryptocurrency wallet. Many wallets will have the option to generate a mnemonic backup phrase, which can be written down on paper. If the user’s computer no longer works or their hard drive becomes corrupted, they can download the same wallet software again and use the mnemonic recovery phrase to restore their keys.

The phrase can be used by anyone to recover the keys, so it must be kept safe. Mnemonic phrases are an excellent way of backing up and storing cryptocurrency and so they are used by almost all wallets.

A mnemonic recovery seed is represented by a group of easy to remember words. For example:

eye female unfair moon genius pipe nuclear width dizzy forum cricket know expire purse laptop scale identify cube pause crucial day cigar noise receive

The above words represent the following seed:

0a5b25e1dab6039d22cd57469744499863962daba9d2844243fec 9c0313c1448d1a0b2cd9e230a78775556f9b514a8be45802c2808e fd449a20234e9262dfa69

These words have certain properties:

  • The first four letters are enough to unambiguously identify the word.
  • Similar words are avoided (such as: build and built).

Bitcoin and most other cryptocurrencies such as Litecoin, Ethereum, and others use mnemonic seeds that are 12 to 24 words long. Other currencies might use different length seeds.

4. Physical backups — Paper, Metal

Some cryptocurrency holders believe that their backup, or even all their cryptocurrency account information, should be stored entirely separately from the internet to avoid any risk of their information being compromised through hacks, exploits, or leaks. This type of storage is called “cold storage.” One method of cold storage involves printing out the keys to a piece of paper and then erasing any record of the keys from all computer systems. The keys can be entered into a program from the paper when needed, or scanned from a QR code printed on the paper.

Printed public and private keys

Printed public and private keys

Some who go to extremes suggest separating the mnemonic needed to access an account into individual pieces of paper and storing those pieces in different locations in the home or office, or even different geographical locations. Some say this is a bad idea since it could be possible to reconstruct the mnemonic from one or more pieces. How diligent you wish to be in protecting these codes is up to you.

Mnemonic recovery phrase booklet

Mnemonic recovery phrase booklet

There’s another option that could make you the envy of your friends. That’s the CryptoSteel wallet, which is a stainless steel metal case that comes with more than 250 stainless steel letter tiles engraved on each side. Codes and passwords are assembled manually from the supplied part-randomized set of tiles. Users are able to store up to 96 characters worth of confidential information. Cryptosteel claims to be fireproof, waterproof, and shock-proof.

image of a Cryptosteel cold storage device

Cryptosteel cold wallet

Of course, if you leave your Cryptosteel wallet in the pocket of a pair of ripped jeans that gets thrown out by the housekeeper, as happened to the character Russ Hanneman on the TV show Silicon Valley in last Sunday’s episode, then you’re out of luck. That fictional billionaire investor lost a USB drive with $300 million in cryptocoins. Let’s hope that doesn’t happen to you.

Encryption & Security

Whether you store your keys on your computer, an external disk, a USB drive, DAS, NAS, or in the cloud, you want to make sure that no one else can use those keys. The best way to handle that is to encrypt the backup.

With Backblaze Backup for Windows and Macintosh, your backups are encrypted in transmission to the cloud and on the backup server. Users have the option to add an additional level of security by adding a Personal Encryption Key (PEK), which secures their private key. Your cryptocurrency backup files are secure in the cloud. Using our web or mobile interface, previous versions of files can be accessed, as well.

Our object storage cloud offering, Backblaze B2, can be used with a variety of applications for Windows, Macintosh, and Linux. With B2, cryptocurrency users can choose whichever method of encryption they wish to use on their local computers and then upload their encrypted currency keys to the cloud. Depending on the client used, versioning and life-cycle rules can be applied to the stored files.

Other backup programs and systems provide some or all of these capabilities, as well. If you are backing up to a local drive, it is a good idea to encrypt the local backup, which is an option in some backup programs.

Address Security

Some experts recommend using a different address for each cryptocurrency transaction. Since the address is not the same as your wallet, this means that you are not creating a new wallet, but simply using a new identifier for people sending you cryptocurrency. Creating a new address is usually as easy as clicking a button in the wallet.

One of the chief advantages of using a different address for each transaction is anonymity. Each time you use an address, you put more information into the public ledger (blockchain) about where the currency came from or where it went. That means that over time, using the same address repeatedly could mean that someone could map your relationships, transactions, and incoming funds. The more you use that address, the more information someone can learn about you. For more on this topic, refer to Address reuse.

Note that a downside of using a paper wallet with a single key pair (type-0 non-deterministic wallet) is that it has the vulnerabilities listed above. Each transaction using that paper wallet will add to the public record of transactions associated with that address. Newer wallets, i.e. “deterministic” or those using mnemonic code words support multiple addresses and are now recommended.

There are other approaches to keeping your cryptocurrency transaction secure. Here are a couple of them.

Multi-signature

Multi-signature refers to requiring more than one key to authorize a transaction, much like requiring more than one key to open a safe. It is generally used to divide up responsibility for possession of cryptocurrency. Standard transactions could be called “single-signature transactions” because transfers require only one signature — from the owner of the private key associated with the currency address (public key). Some wallets and apps can be configured to require more than one signature, which means that a group of people, businesses, or other entities all must agree to trade in the cryptocurrencies.

Deep Cold Storage

Deep cold storage ensures the entire transaction process happens in an offline environment. There are typically three elements to deep cold storage.

First, the wallet and private key are generated offline, and the signing of transactions happens on a system not connected to the internet in any manner. This ensures it’s never exposed to a potentially compromised system or connection.

Second, details are secured with encryption to ensure that even if the wallet file ends up in the wrong hands, the information is protected.

Third, storage of the encrypted wallet file or paper wallet is generally at a location or facility that has restricted access, such as a safety deposit box at a bank.

Deep cold storage is used to safeguard a large individual cryptocurrency portfolio held for the long term, or for trustees holding cryptocurrency on behalf of others, and is possibly the safest method to ensure a crypto investment remains secure.

Keep Your Software Up to Date

You should always make sure that you are using the latest version of your app or wallet software, which includes important stability and security fixes. Installing updates for all other software on your computer or mobile device is also important to keep your wallet environment safer.

One Last Thing: Think About Your Testament

Your cryptocurrency funds can be lost forever if you don’t have a backup plan for your peers and family. If the location of your wallets or your passwords is not known by anyone when you are gone, there is no hope that your funds will ever be recovered. Taking a bit of time on these matters can make a huge difference.

To the Moon*

Are you comfortable with how you’re managing and backing up your cryptocurrency wallets and keys? Do you have a suggestion for keeping your cryptocurrencies safe that we missed above? Please let us know in the comments.


*To the Moon — Crypto slang for a currency that reaches an optimistic price projection.

The post Securing Your Cryptocurrency appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Augmented-reality projection lamp with Raspberry Pi and Android Things

Post Syndicated from Helen Lynn original https://www.raspberrypi.org/blog/augmented-reality-projector/

If your day has been a little fraught so far, watch this video. It opens with a tableau of methodically laid-out components and then shows them soldered, screwed, and slotted neatly into place. Everything fits perfectly; nothing needs percussive adjustment. Then it shows us glimpses of an AR future just like the one promised in the less dystopian comics and TV programmes of my 1980s childhood. It is all very soothing, and exactly what I needed.

Android Things – Lantern

Transform any surface into mixed-reality using Raspberry Pi, a laser projector, and Android Things. Android Experiments – http://experiments.withgoogle.com/android/lantern Lantern project site – http://nordprojects.co/lantern check below to make your own ↓↓↓ Get the code – https://github.com/nordprojects/lantern Build the lamp – https://www.hackster.io/nord-projects/lantern-9f0c28

Creating augmented reality with projection

We’ve seen plenty of Raspberry Pi IoT builds that are smart devices for the home; they add computing power to things like lights, door locks, or toasters to make these objects interact with humans and with their environment in new ways. Nord ProjectsLantern takes a different approach. In their words, it:

imagines a future where projections are used to present ambient information, and relevant UI within everyday objects. Point it at a clock to show your appointments, or point to speaker to display the currently playing song. Unlike a screen, when Lantern’s projections are no longer needed, they simply fade away.

Lantern is set up so that you can connect your wireless device to it using Google Nearby. This means there’s no need to create an account before you can dive into augmented reality.

Lantern Raspberry Pi powered projector lamp

Your own open-source AR lamp

Nord Projects collaborated on Lantern with Google’s Android Things team. They’ve made it fully open-source, so you can find the code on GitHub and also download their parts list, which includes a Pi, an IKEA lamp, an accelerometer, and a laser projector. Build instructions are at hackster.io and on GitHub.

This is a particularly clear tutorial, very well illustrated with photos and GIFs, and once you’ve sourced and 3D-printed all of the components, you shouldn’t need a whole lot of experience to put everything together successfully. Since everything is open-source, though, if you want to adapt it — for example, if you’d like to source a less costly projector than the snazzy one used here — you can do that too.

components of Lantern Raspberry Pi powered augmented reality projector lamp

The instructions walk you through the mechanical build and the wiring, as well as installing Android Things and Nord Projects’ custom software on the Raspberry Pi. Once you’ve set everything up, an accelerometer connected to the Pi’s GPIO pins lets the lamp know which surface it is pointing at. A companion app on your mobile device lets you choose from the mini apps that work on that surface to select the projection you want.

The designers are making several mini apps available for Lantern, including the charmingly named Space Porthole: this uses Processing and your local longitude and latitude to project onto your ceiling the stars you’d see if you punched a hole through to the sky, if it were night time, and clear weather. Wouldn’t you rather look at that than deal with the ant problem in your kitchen or tackle your GitHub notifications?

What would you like to project onto your living environment? Let us know in the comments!

The post Augmented-reality projection lamp with Raspberry Pi and Android Things appeared first on Raspberry Pi.

Firefox 60 released

Post Syndicated from ris original https://lwn.net/Articles/754040/rss

Mozilla has released Firefox 60. From the release
notes
: “Firefox 60 offers something for everyone and a little
something extra for everyone who deploys Firefox in an enterprise environment. This release includes changes that give you more content and more ways to customize your New Tab/Firefox Home. It also introduces support for the Web Authentication API, which means you can log in to websites in Firefox with USB tokens like YubiKey.
Firefox 60 also brings a new policy engine and Group Policy support for
enterprise deployments. For more info about why and how to use Firefox in
the enterprise, see this blog post.

How AWS Meets a Physical Separation Requirement with a Logical Separation Approach

Post Syndicated from Min Hyun original https://aws.amazon.com/blogs/security/how-aws-meets-a-physical-separation-requirement-with-a-logical-separation-approach/

We have a new resource available to help you meet a requirement for physically-separated infrastructure using logical separation in the AWS cloud. Our latest guide, Logical Separation: An Evaluation of the U.S. Department of Defense Cloud Security Requirements for Sensitive Workloads outlines how AWS meets the U.S. Department of Defense’s (DoD) stringent physical separation requirement by pioneering a three-pronged logical separation approach that leverages virtualization, encryption, and deploying compute to dedicated hardware.

This guide will help you understand logical separation in the cloud and demonstrates its advantages over a traditional physical separation model. Embracing this approach can help organizations confidently meet or exceed security requirements found in traditional on-premises environments, while also providing increased security control and flexibility.

Logical Separation is the second guide in the AWS Government Handbook Series, which examines cybersecurity policy initiatives and identifies best practices.

If you have questions or want to learn more, contact your account executive or AWS Support.

Creating a 1.3 Million vCPU Grid on AWS using EC2 Spot Instances and TIBCO GridServer

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/creating-a-1-3-million-vcpu-grid-on-aws-using-ec2-spot-instances-and-tibco-gridserver/

Many of my colleagues are fortunate to be able to spend a good part of their day sitting down with and listening to our customers, doing their best to understand ways that we can better meet their business and technology needs. This information is treated with extreme care and is used to drive the roadmap for new services and new features.

AWS customers in the financial services industry (often abbreviated as FSI) are looking ahead to the Fundamental Review of Trading Book (FRTB) regulations that will come in to effect between 2019 and 2021. Among other things, these regulations mandate a new approach to the “value at risk” calculations that each financial institution must perform in the four hour time window after trading ends in New York and begins in Tokyo. Today, our customers report this mission-critical calculation consumes on the order of 200,000 vCPUs, growing to between 400K and 800K vCPUs in order to meet the FRTB regulations. While there’s still some debate about the magnitude and frequency with which they’ll need to run this expanded calculation, the overall direction is clear.

Building a Big Grid
In order to make sure that we are ready to help our FSI customers meet these new regulations, we worked with TIBCO to set up and run a proof of concept grid in the AWS Cloud. The periodic nature of the calculation, along with the amount of processing power and storage needed to run it to completion within four hours, make it a great fit for an environment where a vast amount of cost-effective compute power is available on an on-demand basis.

Our customers are already using the TIBCO GridServer on-premises and want to use it in the cloud. This product is designed to run grids at enterprise scale. It runs apps in a virtualized fashion, and accepts requests for resources, dynamically provisioning them on an as-needed basis. The cloud version supports Amazon Linux as well as the PostgreSQL-compatible edition of Amazon Aurora.

Working together with TIBCO, we set out to create a grid that was substantially larger than the current high-end prediction of 800K vCPUs, adding a 50% safety factor and then rounding up to reach 1.3 million vCPUs (5x the size of the largest on-premises grid). With that target in mind, the account limits were raised as follows:

  • Spot Instance Limit – 120,000
  • EBS Volume Limit – 120,000
  • EBS Capacity Limit – 2 PB

If you plan to create a grid of this size, you should also bring your friendly local AWS Solutions Architect into the loop as early as possible. They will review your plans, provide you with architecture guidance, and help you to schedule your run.

Running the Grid
We hit the Go button and launched the grid, watching as it bid for and obtained Spot Instances, each of which booted, initialized, and joined the grid within two minutes. The test workload used the Strata open source analytics & market risk library from OpenGamma and was set up with their assistance.

The grid grew to 61,299 Spot Instances (1.3 million vCPUs drawn from 34 instance types spanning 3 generations of EC2 hardware) as planned, with just 1,937 instances reclaimed and automatically replaced during the run, and cost $30,000 per hour to run, at an average hourly cost of $0.078 per vCPU. If the same instances had been used in On-Demand form, the hourly cost to run the grid would have been approximately $93,000.

Despite the scale of the grid, prices for the EC2 instances did not move during the bidding process. This is due to the overall size of the AWS Cloud and the smooth price change model that we launched late last year.

To give you a sense of the compute power, we computed that this grid would have taken the #1 position on the TOP 500 supercomputer list in November 2007 by a considerable margin, and the #2 position in June 2008. Today, it would occupy position #360 on the list.

I hope that you enjoyed this AWS success story, and that it gives you an idea of the scale that you can achieve in the cloud!

Jeff;

What’s new in HiveMQ 3.4

Post Syndicated from The HiveMQ Team original https://www.hivemq.com/whats-new-in-hivemq-3-4

We are pleased to announce the release of HiveMQ 3.4. This version of HiveMQ is the most resilient and advanced version of HiveMQ ever. The main focus in this release was directed towards addressing the needs for the most ambitious MQTT deployments in the world for maximum performance and resilience for millions of concurrent MQTT clients. Of course, deployments of all sizes can profit from the improvements in the latest and greatest HiveMQ.

This version is a drop-in replacement for HiveMQ 3.3 and of course supports rolling upgrades with zero-downtime.

HiveMQ 3.4 brings many features that your users, administrators and plugin developers are going to love. These are the highlights:

 

New HiveMQ 3.4 features at a glance

Cluster

HiveMQ 3.4 brings various improvements in terms of scalability, availability, resilience and observability for the cluster mechanism. Many of the new features remain under the hood, but several additions stand out:

Cluster Overload Protection

The new version has a first-of-its-kind Cluster Overload Protection. The whole cluster is able to spot MQTT clients that cause overload on nodes or the cluster as a whole and protects itself from the overload. This mechanism also protects the deployment from cascading failures due to slow or failing underlying hardware (as sometimes seen on cloud providers). This feature is enabled by default and you can learn more about the mechanism in our documentation.

Dynamic Replicates

HiveMQ’s sophisticated cluster mechanism is able to scale in a linear fashion due to extremely efficient and true data distribution mechanics based on a configured replication factor. The most important aspect of every cluster is availability, which is achieved by having eventual consistency functions in place for edge cases. The 3.4 version adds dynamic replicates to the cluster so even the most challenging edge cases involving network splits don’t lead to the sacrifice of consistency for the most important MQTT operations.

Node Stress Level Metrics

All MQTT cluster nodes are now aware of their own stress level and the stress levels of other cluster members. While all stress mitigation is handled internally by HiveMQ, experienced operators may want to monitor the individual node’s stress level (e.g with Grafana) in order to start investigating what caused the increase of load.

WebUI

Operators worldwide love the HiveMQ WebUI introduced with HiveMQ 3.3. We gathered all the fantastic feedback from our users and polished the WebUI, so it’s even more useful for day-to-day broker operations and remote debugging of MQTT clients. The most important changes and additions are:

Trace Recording Download

The unique Trace Recordings functionality is without doubt a lifesaver when the behavior of individual MQTT clients needs further investigation as all interactions with the broker can be traced — at runtime and at scale! Huge production deployments may accumulate multiple gigabytes of trace recordings. HiveMQ now offers a convenient way to collect all trace recordings from all nodes, zips them and allows the download via a simple button on the WebUI. Remote debugging was never easier!

Additional Client Detail Information in WebUI

The mission of the HiveMQ WebUI is to provide easy insights to the whole production MQTT cluster for operators and administrators. Individual MQTT client investigations are a piece of cake, as all available information about clients can be viewed in detail. We further added the ability to view the restrictions a concrete client has:

  • Maximum Inflight Queue Size
  • Client Offline Queue Messages Size
  • Client Offline Message Drop Strategy

Session Invalidation

MQTT persistent sessions are one of the outstanding features of the MQTT protocol specification. Sessions which do not expire but are never reused unnecessarily consume disk space and memory. Administrators can now invalidate individual session directly in the HiveMQ WebUI for client sessions, which can be deleted safely. HiveMQ 3.4 will take care and release the resources on all cluster nodes after a session was invalidated

Web UI Polishing

Most texts on the WebUI were revisited and are now clearer and crisper. The help texts also received a major overhaul and should now be more, well, helpful. In addition, many small improvements were added, which are most of the time invisible but are here to help when you need them most. For example, the WebUI now displays a warning if cluster nodes with old versions are in the cluster (which may happen if a rolling upgrade was not finished properly)

Plugin System

One of the most popular features of HiveMQ is the extensive Plugin System, which virtually enables the integration of HiveMQ to any system and allows hooking into all aspects of the MQTT lifecycle. We listened to the feedback and are pleased to announce many improvements, big and small, for the Plugin System:

Client Session Time-to-live for individual clients

HiveMQ 3.3 offered a global configuration for setting the Time-To-Live for MQTT sessions. With the advent of HiveMQ 3.4, users can now programmatically set Time-To-Live values for individual MQTT clients and can discard a MQTT session immediately.

Individual Inflight Queues

While the Inflight Queue configuration is typically sufficient in the HiveMQ default configuration, there are some use cases that require the adjustment of this configuration. It’s now possible to change the Inflight Queue size for individual clients via the Plugin System.
 
 

Plugin Service Overload Protection

The HiveMQ Plugin System is a power-user tool and it’s possible to do unbelievably useful modifications as well as putting major stress on the system as a whole if the programmer is not careful. In order to protect the HiveMQ instances from accidental overload, a Plugin Service Overload Protection can be configured. This rate limits the Plugin Service usage and gives feedback to the application programmer in case the rate limit is exceeded. This feature is disabled by default but we strongly recommend updating your plugins to profit from this feature.

Session Attribute Store putIfNewer

This is one of the small bits you almost never need but when you do, you’re ecstatic for being able to use it. The Session Attribute Store now offers methods to put values, if the values you want to put are newer or fresher than the values already written. This is extremely useful, if multiple cluster nodes want to write to the Session Attribute Store simultaneously, as this guarantees that outdated values can no longer overwrite newer values.
 
 
 
 

Disconnection Timestamp for OnDisconnectCallback

As the OnDisconnectCallback is executed asynchronously, the client might already be gone when the callback is executed. It’s now easy to obtain the exact timestamp when a MQTT client disconnected, even if the callback is executed later on. This feature might be very interesting for many plugin developers in conjunction with the Session Attribute Store putIfNewer functionality.

Operations

We ❤️ Operators and we strive to provide all the tools needed for operating and administrating a MQTT broker cluster at scale in any environment. A key strategy for successful operations of any system is monitoring. We added some interesting new metrics you might find useful.

System Metrics

In addition to JVM Metrics, HiveMQ now also gathers Operating System Metrics for Linux Systems. So HiveMQ is able to see for itself how the operating system views the process, including native memory, the real CPU usage, and open file usage. These metrics are particularly useful, if you don’t have a monitoring agent for Linux systems setup. All metrics can be found here.

Client Disconnection Metrics

The reality of many MQTT scenarios is that not all clients are able to disconnect gracefully by sending MQTT DISCONNECT messages. HiveMQ now also exposes metrics about clients that disconnected by closing the TCP connection instead of sending a DISCONNECT packet first. This is especially useful for monitoring, if you regularly deal with clients that don’t have a stable connection to the MQTT brokers.

 

JMX enabled by default

JMX, the Java Monitoring Extension, is now enabled by default. Many HiveMQ operators use Application Performance Monitoring tools, which are able to hook into the metrics via JMX or use plain JMX for on-the-fly debugging. While we recommend to use official off-the-shelf plugins for monitoring, it’s now easier than ever to just use JMX if other solutions are not available to you.

Other notable improvements

The 3.4 release of HiveMQ is full of hidden gems and improvements. While it would be too much to highlight all small improvements, these notable changes stand out and contribute to the best HiveMQ release ever.

Topic Level Distribution Configuration

Our recommendation for all huge deployments with millions of devices is: Start with separate topic prefixes by bringing the dynamic topic parts directly to the beginning. The reality is that many customers have topics that are constructed like the following: “devices/{deviceId}/status”. So what happens is that all topics in this example start with a common prefix, “devices”, which is the first topic level. Unfortunately the first topic level doesn’t include a dynamic topic part. In order to guarantee the best scalability of the cluster and the best performance of the topic tree, customers can now configure how many topic levels are used for distribution. In the example outlined here, a topic level distribution of 2 would be perfect and guarantees the best scalability.

Mass disconnect performance improvements

Mass disconnections of MQTT clients can happen. This might be the case when e.g. a load balancer in front of the MQTT broker cluster drops the connections or if a mobile carrier experiences connectivity problems. Prior to HiveMQ 3.4, mass disconnect events caused stress on the cluster. Mass disconnect events are now massively optimized and even tens of millions of connection losses at the same time won’t bring the cluster into stress situations.

 
 
 
 
 
 

Replication Performance Improvements

Due to the distributed nature of a HiveMQ, data needs to be replicated across the cluster in certain events, e.g. when cluster topology changes occur. There are various internal improvements in HiveMQ version 3.4, which increase the replication performance significantly. Our engineers put special love into the replication of Queued Messages, which is now faster than ever, even for multiple millions of Queued Messages that need to be transferred across the cluster.

Updated Native SSL Libraries

The Native SSL Integration of HiveMQ was updated to the newest BoringSSL version. This results in better performance and increased security. In case you’re using SSL and you are not yet using the native SSL integration, we strongly recommend to give it a try, more than 40% performance improvement can be observed for most deployments.

 
 

Improvements for Java 9

While Java 9 was already supported for older HiveMQ versions, HiveMQ 3.4 has full-blown Java 9 support. The minimum Java version still remains Java 7, although we strongly recommend to use Java 8 or newer for the best performance of HiveMQ.

Ray Ozzie’s Encryption Backdoor

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/05/ray_ozzies_encr.html

Last month, Wired published a long article about Ray Ozzie and his supposed new scheme for adding a backdoor in encrypted devices. It’s a weird article. It paints Ozzie’s proposal as something that “attains the impossible” and “satisfies both law enforcement and privacy purists,” when (1) it’s barely a proposal, and (2) it’s essentially the same key escrow scheme we’ve been hearing about for decades.

Basically, each device has a unique public/private key pair and a secure processor. The public key goes into the processor and the device, and is used to encrypt whatever user key encrypts the data. The private key is stored in a secure database, available to law enforcement on demand. The only other trick is that for law enforcement to use that key, they have to put the device in some sort of irreversible recovery mode, which means it can never be used again. That’s basically it.

I have no idea why anyone is talking as if this were anything new. Several cryptographers have already explained why this key escrow scheme is no better than any other key escrow scheme. The short answer is (1) we won’t be able to secure that database of backdoor keys, (2) we don’t know how to build the secure coprocessor the scheme requires, and (3) it solves none of the policy problems around the whole system. This is the typical mistake non-cryptographers make when they approach this problem: they think that the hard part is the cryptography to create the backdoor. That’s actually the easy part. The hard part is ensuring that it’s only used by the good guys, and there’s nothing in Ozzie’s proposal that addresses any of that.

I worry that this kind of thing is damaging in the long run. There should be some rule that any backdoor or key escrow proposal be a fully specified proposal, not just some cryptography and hand-waving notions about how it will be used in practice. And before it is analyzed and debated, it should have to satisfy some sort of basic security analysis. Otherwise, we’ll be swatting pseudo-proposals like this one, while those on the other side of this debate become increasingly convinced that it’s possible to design one of these things securely.

Already people are using the National Academies report on backdoors for law enforcement as evidence that engineers are developing workable and secure backdoors. Writing in Lawfare, Alan Z. Rozenshtein claims that the report — and a related New York Times story — “undermine the argument that secure third-party access systems are so implausible that it’s not even worth trying to develop them.” Susan Landau effectively corrects this misconception, but the damage is done.

Here’s the thing: it’s not hard to design and build a backdoor. What’s hard is building the systems — both technical and procedural — around them. Here’s Rob Graham:

He’s only solving the part we already know how to solve. He’s deliberately ignoring the stuff we don’t know how to solve. We know how to make backdoors, we just don’t know how to secure them.

A bunch of us cryptographers have already explained why we don’t think this sort of thing will work in the foreseeable future. We write:

Exceptional access would force Internet system developers to reverse “forward secrecy” design practices that seek to minimize the impact on user privacy when systems are breached. The complexity of today’s Internet environment, with millions of apps and globally connected services, means that new law enforcement requirements are likely to introduce unanticipated, hard to detect security flaws. Beyond these and other technical vulnerabilities, the prospect of globally deployed exceptional access systems raises difficult problems about how such an environment would be governed and how to ensure that such systems would respect human rights and the rule of law.

Finally, Matthew Green:

The reason so few of us are willing to bet on massive-scale key escrow systems is that we’ve thought about it and we don’t think it will work. We’ve looked at the threat model, the usage model, and the quality of hardware and software that exists today. Our informed opinion is that there’s no detection system for key theft, there’s no renewability system, HSMs are terrifically vulnerable (and the companies largely staffed with ex-intelligence employees), and insiders can be suborned. We’re not going to put the data of a few billion people on the line an environment where we believe with high probability that the system will fail.

EDITED TO ADD (5/14): An analysis of the proposal.

Bad Software Is Our Fault

Post Syndicated from Bozho original https://techblog.bozho.net/bad-software-is-our-fault/

Bad software is everywhere. One can even claim that every software is bad. Cool companies, tech giants, established companies, all produce bad software. And no, yours is not an exception.

Who’s to blame for bad software? It’s all complicated and many factors are intertwined – there’s business requirements, there’s organizational context, there’s lack of sufficient skilled developers, there’s the inherent complexity of software development, there’s leaky abstractions, reliance on 3rd party software, consequences of wrong business and purchase decisions, time limitations, flawed business analysis, etc. So yes, despite the catchy title, I’m aware it’s actually complicated.

But in every “it’s complicated” scenario, there’s always one or two factors that are decisive. All of them contribute somehow, but the major drivers are usually a handful of things. And in the case of base software, I think it’s the fault of technical people. Developers, architects, ops.

We don’t seem to care about best practices. And I’ll do some nasty generalizations here, but bear with me. We can spend hours arguing about tabs vs spaces, curly bracket on new line, git merge vs rebase, which IDE is better, which framework is better and other largely irrelevant stuff. But we tend to ignore the important aspects that span beyond the code itself. The context in which the code lives, the non-functional requirements – robustness, security, resilience, etc.

We don’t seem to get security. Even trivial stuff such as user authentication is almost always implemented wrong. These days Twitter and GitHub realized they have been logging plain-text passwords, for example, but that’s just the tip of the iceberg. Too often we ignore the security implications.

“But the business didn’t request the security features”, one may say. The business never requested 2-factor authentication, encryption at rest, PKI, secure (or any) audit trail, log masking, crypto shredding, etc., etc. Because the business doesn’t know these things – we do and we have to put them on the backlog and fight for them to be implemented. Each organization has its specifics and tech people can influence the backlog in different ways, but almost everywhere we can put things there and prioritize them.

The other aspect is testing. We should all be well aware by now that automated testing is mandatory. We have all the tools in the world for unit, functional, integration, performance and whatnot testing, and yet many software projects lack the necessary test coverage to be able to change stuff without accidentally breaking things. “But testing takes time, we don’t have it”. We are perfectly aware that testing saves time, as we’ve all had those “not again!” recurring bugs. And yet we think of all sorts of excuses – “let the QAs test it”, we have to ship that now, we’ll test it later”, “this is too trivial to be tested”, etc.

And you may say it’s not our job. We don’t define what has do be done, we just do it. We don’t define the budget, the scope, the features. We just write whatever has been decided. And that’s plain wrong. It’s not our job to make money out of our code, and it’s not our job to define what customers need, but apart from that everything is our job. The way the software is structured, the security aspects and security features, the stability of the code base, the way the software behaves in different environments. The non-functional requirements are our job, and putting them on the backlog is our job.

You’ve probably heard that every software becomes “legacy” after 6 months. And that’s because of us, our sloppiness, our inability to mitigate external factors and constraints. Too often we create a mess through “just doing our job”.

And of course that’s a generalization. I happen to know a lot of great professionals who don’t make these mistakes, who strive for excellence and implement things the right way. But our industry as a whole doesn’t. Our industry as a whole produces bad software. And it’s our fault, as developers – as the only people who know why a certain piece of software is bad.

In a talk of his, Bob Martin warns us of the risks of our sloppiness. We have been building websites so far, but we are more and more building stuff that interacts with the real world, directly and indirectly. Ultimately, lives may depend on our software (like the recent unfortunate death caused by a self-driving car). And I’ll agree with Uncle Bob that it’s high time we self-regulate as an industry, before some technically incompetent politician decides to do that.

How, I don’t know. We’ll have to think more about it. But I’m pretty sure it’s our fault that software is bad, and no amount of blaming the management, the budget, the timing, the tools or the process can eliminate our responsibility.

Why do I insist on bashing my fellow software engineers? Because if we start looking at software development with more responsibility; with the fact that if it fails, it’s our fault, then we’re more likely to get out of our current bug-ridden, security-flawed, fragile software hole and really become the experts of the future.

The post Bad Software Is Our Fault appeared first on Bozho's tech blog.

Announcing Local Build Support for AWS CodeBuild

Post Syndicated from Karthik Thirugnanasambandam original https://aws.amazon.com/blogs/devops/announcing-local-build-support-for-aws-codebuild/

Today, we’re excited to announce local build support in AWS CodeBuild.

AWS CodeBuild is a fully managed build service. There are no servers to provision and scale, or software to install, configure, and operate. You just specify the location of your source code, choose your build settings, and CodeBuild runs build scripts for compiling, testing, and packaging your code.

In this blog post, I’ll show you how to set up CodeBuild locally to build and test a sample Java application.

By building an application on a local machine you can:

  • Test the integrity and contents of a buildspec file locally.
  • Test and build an application locally before committing.
  • Identify and fix errors quickly from your local development environment.

Prerequisites

In this post, I am using AWS Cloud9 IDE as my development environment.

If you would like to use AWS Cloud9 as your IDE, follow the express setup steps in the AWS Cloud9 User Guide.

The AWS Cloud9 IDE comes with Docker and Git already installed. If you are going to use your laptop or desktop machine as your development environment, install Docker and Git before you start.

Steps to build CodeBuild image locally

Run git clone https://github.com/aws/aws-codebuild-docker-images.git to download this repository to your local machine.

$ git clone https://github.com/aws/aws-codebuild-docker-images.git

Lets build a local CodeBuild image for JDK 8 environment. The Dockerfile for JDK 8 is present in /aws-codebuild-docker-images/ubuntu/java/openjdk-8.

Edit the Dockerfile to remove the last line ENTRYPOINT [“dockerd-entrypoint.sh”] and save the file.

Run cd ubuntu/java/openjdk-8 to change the directory in your local workspace.

Run docker build -t aws/codebuild/java:openjdk-8 . to build the Docker image locally. This command will take few minutes to complete.

$ cd aws-codebuild-docker-images
$ cd ubuntu/java/openjdk-8
$ docker build -t aws/codebuild/java:openjdk-8 .

Steps to setup CodeBuild local agent

Run the following Docker pull command to download the local CodeBuild agent.

$ docker pull amazon/aws-codebuild-local:latest --disable-content-trust=false

Now you have the local agent image on your machine and can run a local build.

Run the following git command to download a sample Java project.

$ git clone https://github.com/karthiksambandam/sample-web-app.git

Steps to use the local agent to build a sample project

Let’s build the sample Java project using the local agent.

Execute the following Docker command to run the local agent and build the sample web app repository you cloned earlier.

$ docker run -it -v /var/run/docker.sock:/var/run/docker.sock -e "IMAGE_NAME=aws/codebuild/java:openjdk-8" -e "ARTIFACTS=/home/ec2-user/environment/artifacts" -e "SOURCE=/home/ec2-user/environment/sample-web-app" amazon/aws-codebuild-local

Note: We need to provide three environment variables namely  IMAGE_NAME, SOURCE and ARTIFACTS.

IMAGE_NAME: The name of your build environment image.

SOURCE: The absolute path to your source code directory.

ARTIFACTS: The absolute path to your artifact output folder.

When you run the sample project, you get a runtime error that says the YAML file does not exist. This is because a buildspec.yml file is not included in the sample web project. AWS CodeBuild requires a buildspec.yml to run a build. For more information about buildspec.yml, see Build Spec Example in the AWS CodeBuild User Guide.

Let’s add a buildspec.yml file with the following content to the sample-web-app folder and then rebuild the project.

version: 0.2

phases:
  build:
    commands:
      - echo Build started on `date`
      - mvn install

artifacts:
  files:
    - target/javawebdemo.war

$ docker run -it -v /var/run/docker.sock:/var/run/docker.sock -e "IMAGE_NAME=aws/codebuild/java:openjdk-8" -e "ARTIFACTS=/home/ec2-user/environment/artifacts" -e "SOURCE=/home/ec2-user/environment/sample-web-app" amazon/aws-codebuild-local

This time your build should be successful. Upon successful execution, look in the /artifacts folder for the final built artifacts.zip file to validate.

Conclusion:

In this blog post, I showed you how to quickly set up the CodeBuild local agent to build projects right from your local desktop machine or laptop. As you see, local builds can improve developer productivity by helping you identify and fix errors quickly.

I hope you found this post useful. Feel free to leave your feedback or suggestions in the comments.

Analyze data in Amazon DynamoDB using Amazon SageMaker for real-time prediction

Post Syndicated from YongSeong Lee original https://aws.amazon.com/blogs/big-data/analyze-data-in-amazon-dynamodb-using-amazon-sagemaker-for-real-time-prediction/

Many companies across the globe use Amazon DynamoDB to store and query historical user-interaction data. DynamoDB is a fast NoSQL database used by applications that need consistent, single-digit millisecond latency.

Often, customers want to turn their valuable data in DynamoDB into insights by analyzing a copy of their table stored in Amazon S3. Doing this separates their analytical queries from their low-latency critical paths. This data can be the primary source for understanding customers’ past behavior, predicting future behavior, and generating downstream business value. Customers often turn to DynamoDB because of its great scalability and high availability. After a successful launch, many customers want to use the data in DynamoDB to predict future behaviors or provide personalized recommendations.

DynamoDB is a good fit for low-latency reads and writes, but it’s not practical to scan all data in a DynamoDB database to train a model. In this post, I demonstrate how you can use DynamoDB table data copied to Amazon S3 by AWS Data Pipeline to predict customer behavior. I also demonstrate how you can use this data to provide personalized recommendations for customers using Amazon SageMaker. You can also run ad hoc queries using Amazon Athena against the data. DynamoDB recently released on-demand backups to create full table backups with no performance impact. However, it’s not suitable for our purposes in this post, so I chose AWS Data Pipeline instead to create managed backups are accessible from other services.

To do this, I describe how to read the DynamoDB backup file format in Data Pipeline. I also describe how to convert the objects in S3 to a CSV format that Amazon SageMaker can read. In addition, I show how to schedule regular exports and transformations using Data Pipeline. The sample data used in this post is from Bank Marketing Data Set of UCI.

The solution that I describe provides the following benefits:

  • Separates analytical queries from production traffic on your DynamoDB table, preserving your DynamoDB read capacity units (RCUs) for important production requests
  • Automatically updates your model to get real-time predictions
  • Optimizes for performance (so it doesn’t compete with DynamoDB RCUs after the export) and for cost (using data you already have)
  • Makes it easier for developers of all skill levels to use Amazon SageMaker

All code and data set in this post are available in this .zip file.

Solution architecture

The following diagram shows the overall architecture of the solution.

The steps that data follows through the architecture are as follows:

  1. Data Pipeline regularly copies the full contents of a DynamoDB table as JSON into an S3
  2. Exported JSON files are converted to comma-separated value (CSV) format to use as a data source for Amazon SageMaker.
  3. Amazon SageMaker renews the model artifact and update the endpoint.
  4. The converted CSV is available for ad hoc queries with Amazon Athena.
  5. Data Pipeline controls this flow and repeats the cycle based on the schedule defined by customer requirements.

Building the auto-updating model

This section discusses details about how to read the DynamoDB exported data in Data Pipeline and build automated workflows for real-time prediction with a regularly updated model.

Download sample scripts and data

Before you begin, take the following steps:

  1. Download sample scripts in this .zip file.
  2. Unzip the src.zip file.
  3. Find the automation_script.sh file and edit it for your environment. For example, you need to replace 's3://<your bucket>/<datasource path>/' with your own S3 path to the data source for Amazon ML. In the script, the text enclosed by angle brackets—< and >—should be replaced with your own path.
  4. Upload the json-serde-1.3.6-SNAPSHOT-jar-with-dependencies.jar file to your S3 path so that the ADD jar command in Apache Hive can refer to it.

For this solution, the banking.csv  should be imported into a DynamoDB table.

Export a DynamoDB table

To export the DynamoDB table to S3, open the Data Pipeline console and choose the Export DynamoDB table to S3 template. In this template, Data Pipeline creates an Amazon EMR cluster and performs an export in the EMRActivity activity. Set proper intervals for backups according to your business requirements.

One core node(m3.xlarge) provides the default capacity for the EMR cluster and should be suitable for the solution in this post. Leave the option to resize the cluster before running enabled in the TableBackupActivity activity to let Data Pipeline scale the cluster to match the table size. The process of converting to CSV format and renewing models happens in this EMR cluster.

For a more in-depth look at how to export data from DynamoDB, see Export Data from DynamoDB in the Data Pipeline documentation.

Add the script to an existing pipeline

After you export your DynamoDB table, you add an additional EMR step to EMRActivity by following these steps:

  1. Open the Data Pipeline console and choose the ID for the pipeline that you want to add the script to.
  2. For Actions, choose Edit.
  3. In the editing console, choose the Activities category and add an EMR step using the custom script downloaded in the previous section, as shown below.

Paste the following command into the new step after the data ­­upload step:

s3://#{myDDBRegion}.elasticmapreduce/libs/script-runner/script-runner.jar,s3://<your bucket name>/automation_script.sh,#{output.directoryPath},#{myDDBRegion}

The element #{output.directoryPath} references the S3 path where the data pipeline exports DynamoDB data as JSON. The path should be passed to the script as an argument.

The bash script has two goals, converting data formats and renewing the Amazon SageMaker model. Subsequent sections discuss the contents of the automation script.

Automation script: Convert JSON data to CSV with Hive

We use Apache Hive to transform the data into a new format. The Hive QL script to create an external table and transform the data is included in the custom script that you added to the Data Pipeline definition.

When you run the Hive scripts, do so with the -e option. Also, define the Hive table with the 'org.openx.data.jsonserde.JsonSerDe' row format to parse and read JSON format. The SQL creates a Hive EXTERNAL table, and it reads the DynamoDB backup data on the S3 path passed to it by Data Pipeline.

Note: You should create the table with the “EXTERNAL” keyword to avoid the backup data being accidentally deleted from S3 if you drop the table.

The full automation script for converting follows. Add your own bucket name and data source path in the highlighted areas.

#!/bin/bash
hive -e "
ADD jar s3://<your bucket name>/json-serde-1.3.6-SNAPSHOT-jar-with-dependencies.jar ; 
DROP TABLE IF EXISTS blog_backup_data ;
CREATE EXTERNAL TABLE blog_backup_data (
 customer_id map<string,string>,
 age map<string,string>, job map<string,string>, 
 marital map<string,string>,education map<string,string>, 
 default map<string,string>, housing map<string,string>,
 loan map<string,string>, contact map<string,string>, 
 month map<string,string>, day_of_week map<string,string>, 
 duration map<string,string>, campaign map<string,string>,
 pdays map<string,string>, previous map<string,string>, 
 poutcome map<string,string>, emp_var_rate map<string,string>, 
 cons_price_idx map<string,string>, cons_conf_idx map<string,string>,
 euribor3m map<string,string>, nr_employed map<string,string>, 
 y map<string,string> ) 
ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe' 
LOCATION '$1/';

INSERT OVERWRITE DIRECTORY 's3://<your bucket name>/<datasource path>/' 
SELECT concat( customer_id['s'],',', 
 age['n'],',', job['s'],',', 
 marital['s'],',', education['s'],',', default['s'],',', 
 housing['s'],',', loan['s'],',', contact['s'],',', 
 month['s'],',', day_of_week['s'],',', duration['n'],',', 
 campaign['n'],',',pdays['n'],',',previous['n'],',', 
 poutcome['s'],',', emp_var_rate['n'],',', cons_price_idx['n'],',',
 cons_conf_idx['n'],',', euribor3m['n'],',', nr_employed['n'],',', y['n'] ) 
FROM blog_backup_data
WHERE customer_id['s'] > 0 ; 

After creating an external table, you need to read data. You then use the INSERT OVERWRITE DIRECTORY ~ SELECT command to write CSV data to the S3 path that you designated as the data source for Amazon SageMaker.

Depending on your requirements, you can eliminate or process the columns in the SELECT clause in this step to optimize data analysis. For example, you might remove some columns that have unpredictable correlations with the target value because keeping the wrong columns might expose your model to “overfitting” during the training. In this post, customer_id  columns is removed. Overfitting can make your prediction weak. More information about overfitting can be found in the topic Model Fit: Underfitting vs. Overfitting in the Amazon ML documentation.

Automation script: Renew the Amazon SageMaker model

After the CSV data is replaced and ready to use, create a new model artifact for Amazon SageMaker with the updated dataset on S3.  For renewing model artifact, you must create a new training job.  Training jobs can be run using the AWS SDK ( for example, Amazon SageMaker boto3 ) or the Amazon SageMaker Python SDK that can be installed with “pip install sagemaker” command as well as the AWS CLI for Amazon SageMaker described in this post.

In addition, consider how to smoothly renew your existing model without service impact, because your model is called by applications in real time. To do this, you need to create a new endpoint configuration first and update a current endpoint with the endpoint configuration that is just created.

#!/bin/bash
## Define variable 
REGION=$2
DTTIME=`date +%Y-%m-%d-%H-%M-%S`
ROLE="<your AmazonSageMaker-ExecutionRole>" 


# Select containers image based on region.  
case "$REGION" in
"us-west-2" )
    IMAGE="174872318107.dkr.ecr.us-west-2.amazonaws.com/linear-learner:latest"
    ;;
"us-east-1" )
    IMAGE="382416733822.dkr.ecr.us-east-1.amazonaws.com/linear-learner:latest" 
    ;;
"us-east-2" )
    IMAGE="404615174143.dkr.ecr.us-east-2.amazonaws.com/linear-learner:latest" 
    ;;
"eu-west-1" )
    IMAGE="438346466558.dkr.ecr.eu-west-1.amazonaws.com/linear-learner:latest" 
    ;;
 *)
    echo "Invalid Region Name"
    exit 1 ;  
esac

# Start training job and creating model artifact 
TRAINING_JOB_NAME=TRAIN-${DTTIME} 
S3OUTPUT="s3://<your bucket name>/model/" 
INSTANCETYPE="ml.m4.xlarge"
INSTANCECOUNT=1
VOLUMESIZE=5 
aws sagemaker create-training-job --training-job-name ${TRAINING_JOB_NAME} --region ${REGION}  --algorithm-specification TrainingImage=${IMAGE},TrainingInputMode=File --role-arn ${ROLE}  --input-data-config '[{ "ChannelName": "train", "DataSource": { "S3DataSource": { "S3DataType": "S3Prefix", "S3Uri": "s3://<your bucket name>/<datasource path>/", "S3DataDistributionType": "FullyReplicated" } }, "ContentType": "text/csv", "CompressionType": "None" , "RecordWrapperType": "None"  }]'  --output-data-config S3OutputPath=${S3OUTPUT} --resource-config  InstanceType=${INSTANCETYPE},InstanceCount=${INSTANCECOUNT},VolumeSizeInGB=${VOLUMESIZE} --stopping-condition MaxRuntimeInSeconds=120 --hyper-parameters feature_dim=20,predictor_type=binary_classifier  

# Wait until job completed 
aws sagemaker wait training-job-completed-or-stopped --training-job-name ${TRAINING_JOB_NAME}  --region ${REGION}

# Get newly created model artifact and create model
MODELARTIFACT=`aws sagemaker describe-training-job --training-job-name ${TRAINING_JOB_NAME} --region ${REGION}  --query 'ModelArtifacts.S3ModelArtifacts' --output text `
MODELNAME=MODEL-${DTTIME}
aws sagemaker create-model --region ${REGION} --model-name ${MODELNAME}  --primary-container Image=${IMAGE},ModelDataUrl=${MODELARTIFACT}  --execution-role-arn ${ROLE}

# create a new endpoint configuration 
CONFIGNAME=CONFIG-${DTTIME}
aws sagemaker  create-endpoint-config --region ${REGION} --endpoint-config-name ${CONFIGNAME}  --production-variants  VariantName=Users,ModelName=${MODELNAME},InitialInstanceCount=1,InstanceType=ml.m4.xlarge

# create or update the endpoint
STATUS=`aws sagemaker describe-endpoint --endpoint-name  ServiceEndpoint --query 'EndpointStatus' --output text --region ${REGION} `
if [[ $STATUS -ne "InService" ]] ;
then
    aws sagemaker  create-endpoint --endpoint-name  ServiceEndpoint  --endpoint-config-name ${CONFIGNAME} --region ${REGION}    
else
    aws sagemaker  update-endpoint --endpoint-name  ServiceEndpoint  --endpoint-config-name ${CONFIGNAME} --region ${REGION}
fi

Grant permission

Before you execute the script, you must grant proper permission to Data Pipeline. Data Pipeline uses the DataPipelineDefaultResourceRole role by default. I added the following policy to DataPipelineDefaultResourceRole to allow Data Pipeline to create, delete, and update the Amazon SageMaker model and data source in the script.

{
 "Version": "2012-10-17",
 "Statement": [
 {
 "Effect": "Allow",
 "Action": [
 "sagemaker:CreateTrainingJob",
 "sagemaker:DescribeTrainingJob",
 "sagemaker:CreateModel",
 "sagemaker:CreateEndpointConfig",
 "sagemaker:DescribeEndpoint",
 "sagemaker:CreateEndpoint",
 "sagemaker:UpdateEndpoint",
 "iam:PassRole"
 ],
 "Resource": "*"
 }
 ]
}

Use real-time prediction

After you deploy a model into production using Amazon SageMaker hosting services, your client applications use this API to get inferences from the model hosted at the specified endpoint. This approach is useful for interactive web, mobile, or desktop applications.

Following, I provide a simple Python code example that queries against Amazon SageMaker endpoint URL with its name (“ServiceEndpoint”) and then uses them for real-time prediction.

=== Python sample for real-time prediction ===

#!/usr/bin/env python
import boto3
import json 

client = boto3.client('sagemaker-runtime', region_name ='<your region>' )
new_customer_info = '34,10,2,4,1,2,1,1,6,3,190,1,3,4,3,-1.7,94.055,-39.8,0.715,4991.6'
response = client.invoke_endpoint(
    EndpointName='ServiceEndpoint',
    Body=new_customer_info, 
    ContentType='text/csv'
)
result = json.loads(response['Body'].read().decode())
print(result)
--- output(response) ---
{u'predictions': [{u'score': 0.7528127431869507, u'predicted_label': 1.0}]}

Solution summary

The solution takes the following steps:

  1. Data Pipeline exports DynamoDB table data into S3. The original JSON data should be kept to recover the table in the rare event that this is needed. Data Pipeline then converts JSON to CSV so that Amazon SageMaker can read the data.Note: You should select only meaningful attributes when you convert CSV. For example, if you judge that the “campaign” attribute is not correlated, you can eliminate this attribute from the CSV.
  2. Train the Amazon SageMaker model with the new data source.
  3. When a new customer comes to your site, you can judge how likely it is for this customer to subscribe to your new product based on “predictedScores” provided by Amazon SageMaker.
  4. If the new user subscribes your new product, your application must update the attribute “y” to the value 1 (for yes). This updated data is provided for the next model renewal as a new data source. It serves to improve the accuracy of your prediction. With each new entry, your application can become smarter and deliver better predictions.

Running ad hoc queries using Amazon Athena

Amazon Athena is a serverless query service that makes it easy to analyze large amounts of data stored in Amazon S3 using standard SQL. Athena is useful for examining data and collecting statistics or informative summaries about data. You can also use the powerful analytic functions of Presto, as described in the topic Aggregate Functions of Presto in the Presto documentation.

With the Data Pipeline scheduled activity, recent CSV data is always located in S3 so that you can run ad hoc queries against the data using Amazon Athena. I show this with example SQL statements following. For an in-depth description of this process, see the post Interactive SQL Queries for Data in Amazon S3 on the AWS News Blog. 

Creating an Amazon Athena table and running it

Simply, you can create an EXTERNAL table for the CSV data on S3 in Amazon Athena Management Console.

=== Table Creation ===
CREATE EXTERNAL TABLE datasource (
 age int, 
 job string, 
 marital string , 
 education string, 
 default string, 
 housing string, 
 loan string, 
 contact string, 
 month string, 
 day_of_week string, 
 duration int, 
 campaign int, 
 pdays int , 
 previous int , 
 poutcome string, 
 emp_var_rate double, 
 cons_price_idx double,
 cons_conf_idx double, 
 euribor3m double, 
 nr_employed double, 
 y int 
)
ROW FORMAT DELIMITED 
FIELDS TERMINATED BY ',' ESCAPED BY '\\' LINES TERMINATED BY '\n' 
LOCATION 's3://<your bucket name>/<datasource path>/';

The following query calculates the correlation coefficient between the target attribute and other attributes using Amazon Athena.

=== Sample Query ===

SELECT corr(age,y) AS correlation_age_and_target, 
 corr(duration,y) AS correlation_duration_and_target, 
 corr(campaign,y) AS correlation_campaign_and_target,
 corr(contact,y) AS correlation_contact_and_target
FROM ( SELECT age , duration , campaign , y , 
 CASE WHEN contact = 'telephone' THEN 1 ELSE 0 END AS contact 
 FROM datasource 
 ) datasource ;

Conclusion

In this post, I introduce an example of how to analyze data in DynamoDB by using table data in Amazon S3 to optimize DynamoDB table read capacity. You can then use the analyzed data as a new data source to train an Amazon SageMaker model for accurate real-time prediction. In addition, you can run ad hoc queries against the data on S3 using Amazon Athena. I also present how to automate these procedures by using Data Pipeline.

You can adapt this example to your specific use case at hand, and hopefully this post helps you accelerate your development. You can find more examples and use cases for Amazon SageMaker in the video AWS 2017: Introducing Amazon SageMaker on the AWS website.

 


Additional Reading

If you found this post useful, be sure to check out Serving Real-Time Machine Learning Predictions on Amazon EMR and Analyzing Data in S3 using Amazon Athena.

 


About the Author

Yong Seong Lee is a Cloud Support Engineer for AWS Big Data Services. He is interested in every technology related to data/databases and helping customers who have difficulties in using AWS services. His motto is “Enjoy life, be curious and have maximum experience.”

 

 

[$] Toward better performance on large-memory systems

Post Syndicated from corbet original https://lwn.net/Articles/753171/rss

Christoph Lameter works in a different computing environment than most of
us; he supports high-volume trading applications that need every bit of
performance that the fastest hardware can give them. Even then, it seems
that isn’t fast enough. In a memory-management-track session at the 2018
Linux Storage, Filesystem, and Memory-Management Summit, Lameter described
some of the problems he has encountered and approaches he is considering to
address them.